Overview

Dataset statistics

Number of variables20
Number of observations41188
Missing cells8430
Missing cells (%)1.0%
Duplicate rows1054
Duplicate rows (%)2.6%
Total size in memory6.3 MiB
Average record size in memory160.0 B

Variable types

Numeric9
Categorical10
Boolean1

Alerts

Dataset has 1054 (2.6%) duplicate rowsDuplicates
cons.conf.idx is highly overall correlated with monthHigh correlation
cons.price.idx is highly overall correlated with contact and 2 other fieldsHigh correlation
contact is highly overall correlated with cons.price.idx and 1 other fieldsHigh correlation
emp.var.rate is highly overall correlated with cons.price.idx and 3 other fieldsHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
housing is highly overall correlated with loanHigh correlation
loan is highly overall correlated with housingHigh correlation
month is highly overall correlated with cons.conf.idx and 4 other fieldsHigh correlation
nr.employed is highly overall correlated with emp.var.rate and 1 other fieldsHigh correlation
pdays is highly overall correlated with poutcome and 1 other fieldsHigh correlation
poutcome is highly overall correlated with pdaysHigh correlation
previous is highly overall correlated with pdaysHigh correlation
default is highly imbalanced (53.3%)Imbalance
loan is highly imbalanced (51.3%)Imbalance
poutcome is highly imbalanced (56.8%)Imbalance
age has 421 (1.0%) missing valuesMissing
job has 484 (1.2%) missing valuesMissing
marital has 413 (1.0%) missing valuesMissing
education has 424 (1.0%) missing valuesMissing
loan has 455 (1.1%) missing valuesMissing
contact has 440 (1.1%) missing valuesMissing
month has 421 (1.0%) missing valuesMissing
day_of_week has 436 (1.1%) missing valuesMissing
campaign has 413 (1.0%) missing valuesMissing
pdays has 449 (1.1%) missing valuesMissing
previous has 418 (1.0%) missing valuesMissing
poutcome has 431 (1.0%) missing valuesMissing
emp.var.rate has 418 (1.0%) missing valuesMissing
euribor3m has 429 (1.0%) missing valuesMissing
nr.employed has 437 (1.1%) missing valuesMissing
previous has 35209 (85.5%) zerosZeros

Reproduction

Analysis started2025-02-14 18:45:14.787479
Analysis finished2025-02-14 18:45:41.333970
Duration26.55 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

age
Real number (ℝ)

MISSING 

Distinct78
Distinct (%)0.2%
Missing421
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean40.02112
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2025-02-15T00:15:41.478670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.419903
Coefficient of variation (CV)0.2603601
Kurtosis0.79486589
Mean40.02112
Median Absolute Deviation (MAD)7
Skewness0.78448388
Sum1631541
Variance108.57437
MonotonicityNot monotonic
2025-02-15T00:15:41.669168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 1929
 
4.7%
32 1825
 
4.4%
33 1814
 
4.4%
36 1755
 
4.3%
35 1739
 
4.2%
34 1724
 
4.2%
30 1698
 
4.1%
37 1463
 
3.6%
29 1433
 
3.5%
39 1426
 
3.5%
Other values (68) 23961
58.2%
ValueCountFrequency (%)
17 5
 
< 0.1%
18 28
 
0.1%
19 42
 
0.1%
20 65
 
0.2%
21 101
 
0.2%
22 137
 
0.3%
23 225
 
0.5%
24 458
1.1%
25 593
1.4%
26 690
1.7%
ValueCountFrequency (%)
98 2
 
< 0.1%
95 1
 
< 0.1%
94 1
 
< 0.1%
92 4
 
< 0.1%
91 2
 
< 0.1%
89 2
 
< 0.1%
88 22
0.1%
87 1
 
< 0.1%
86 8
 
< 0.1%
85 15
< 0.1%

job
Categorical

MISSING 

Distinct12
Distinct (%)< 0.1%
Missing484
Missing (%)1.2%
Memory size321.9 KiB
admin.
10315 
blue-collar
9126 
technician
6664 
services
3917 
management
2890 
Other values (7)
7792 

Length

Max length13
Median length12
Mean length8.9539849
Min length6

Characters and Unicode

Total characters364463
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousemaid
2nd rowservices
3rd rowservices
4th rowadmin.
5th rowservices

Common Values

ValueCountFrequency (%)
admin. 10315
25.0%
blue-collar 9126
22.2%
technician 6664
16.2%
services 3917
 
9.5%
management 2890
 
7.0%
retired 1698
 
4.1%
entrepreneur 1441
 
3.5%
self-employed 1408
 
3.4%
housemaid 1051
 
2.6%
unemployed 1004
 
2.4%
Other values (2) 1190
 
2.9%

Length

2025-02-15T00:15:41.867360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 10315
25.3%
blue-collar 9126
22.4%
technician 6664
16.4%
services 3917
 
9.6%
management 2890
 
7.1%
retired 1698
 
4.2%
entrepreneur 1441
 
3.5%
self-employed 1408
 
3.5%
housemaid 1051
 
2.6%
unemployed 1004
 
2.5%
Other values (2) 1190
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 46710
12.8%
n 35153
 
9.6%
a 32936
 
9.0%
l 31198
 
8.6%
i 30309
 
8.3%
c 26371
 
7.2%
r 20762
 
5.7%
m 19558
 
5.4%
d 16339
 
4.5%
t 14419
 
4.0%
Other values (14) 90708
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 343614
94.3%
Dash Punctuation 10534
 
2.9%
Other Punctuation 10315
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 46710
13.6%
n 35153
10.2%
a 32936
9.6%
l 31198
9.1%
i 30309
8.8%
c 26371
 
7.7%
r 20762
 
6.0%
m 19558
 
5.7%
d 16339
 
4.8%
t 14419
 
4.2%
Other values (12) 69859
20.3%
Dash Punctuation
ValueCountFrequency (%)
- 10534
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10315
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 343614
94.3%
Common 20849
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 46710
13.6%
n 35153
10.2%
a 32936
9.6%
l 31198
9.1%
i 30309
8.8%
c 26371
 
7.7%
r 20762
 
6.0%
m 19558
 
5.7%
d 16339
 
4.8%
t 14419
 
4.2%
Other values (12) 69859
20.3%
Common
ValueCountFrequency (%)
- 10534
50.5%
. 10315
49.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 364463
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 46710
12.8%
n 35153
 
9.6%
a 32936
 
9.0%
l 31198
 
8.6%
i 30309
 
8.3%
c 26371
 
7.2%
r 20762
 
5.7%
m 19558
 
5.4%
d 16339
 
4.5%
t 14419
 
4.0%
Other values (14) 90708
24.9%

marital
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing413
Missing (%)1.0%
Memory size321.9 KiB
married
24670 
single
11452 
divorced
4574 
unknown
 
79

Length

Max length8
Median length7
Mean length6.8313182
Min length6

Characters and Unicode

Total characters278547
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married 24670
59.9%
single 11452
27.8%
divorced 4574
 
11.1%
unknown 79
 
0.2%
(Missing) 413
 
1.0%

Length

2025-02-15T00:15:42.059863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T00:15:42.217964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
married 24670
60.5%
single 11452
28.1%
divorced 4574
 
11.2%
unknown 79
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 53914
19.4%
i 40696
14.6%
e 40696
14.6%
d 33818
12.1%
m 24670
8.9%
a 24670
8.9%
n 11689
 
4.2%
s 11452
 
4.1%
g 11452
 
4.1%
l 11452
 
4.1%
Other values (6) 14038
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 278547
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 53914
19.4%
i 40696
14.6%
e 40696
14.6%
d 33818
12.1%
m 24670
8.9%
a 24670
8.9%
n 11689
 
4.2%
s 11452
 
4.1%
g 11452
 
4.1%
l 11452
 
4.1%
Other values (6) 14038
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 278547
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 53914
19.4%
i 40696
14.6%
e 40696
14.6%
d 33818
12.1%
m 24670
8.9%
a 24670
8.9%
n 11689
 
4.2%
s 11452
 
4.1%
g 11452
 
4.1%
l 11452
 
4.1%
Other values (6) 14038
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278547
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 53914
19.4%
i 40696
14.6%
e 40696
14.6%
d 33818
12.1%
m 24670
8.9%
a 24670
8.9%
n 11689
 
4.2%
s 11452
 
4.1%
g 11452
 
4.1%
l 11452
 
4.1%
Other values (6) 14038
 
5.0%

education
Categorical

MISSING 

Distinct8
Distinct (%)< 0.1%
Missing424
Missing (%)1.0%
Memory size321.9 KiB
university.degree
12039 
high.school
9417 
basic.9y
5985 
professional.course
5185 
basic.4y
4140 
Other values (3)
3998 

Length

Max length19
Median length17
Mean length12.708983
Min length7

Characters and Unicode

Total characters518069
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.4y
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic.6y
5th rowhigh.school

Common Values

ValueCountFrequency (%)
university.degree 12039
29.2%
high.school 9417
22.9%
basic.9y 5985
14.5%
professional.course 5185
12.6%
basic.4y 4140
 
10.1%
basic.6y 2264
 
5.5%
unknown 1716
 
4.2%
illiterate 18
 
< 0.1%
(Missing) 424
 
1.0%

Length

2025-02-15T00:15:42.377050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T00:15:42.536128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 12039
29.5%
high.school 9417
23.1%
basic.9y 5985
14.7%
professional.course 5185
12.7%
basic.4y 4140
 
10.2%
basic.6y 2264
 
5.6%
unknown 1716
 
4.2%
illiterate 18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 58562
 
11.3%
i 51105
 
9.9%
s 49400
 
9.5%
. 39030
 
7.5%
o 36105
 
7.0%
r 34466
 
6.7%
h 28251
 
5.5%
c 26991
 
5.2%
y 24428
 
4.7%
n 22372
 
4.3%
Other values (15) 147359
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 466650
90.1%
Other Punctuation 39030
 
7.5%
Decimal Number 12389
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 58562
12.5%
i 51105
11.0%
s 49400
10.6%
o 36105
 
7.7%
r 34466
 
7.4%
h 28251
 
6.1%
c 26991
 
5.8%
y 24428
 
5.2%
n 22372
 
4.8%
g 21456
 
4.6%
Other values (11) 113514
24.3%
Decimal Number
ValueCountFrequency (%)
9 5985
48.3%
4 4140
33.4%
6 2264
 
18.3%
Other Punctuation
ValueCountFrequency (%)
. 39030
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 466650
90.1%
Common 51419
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 58562
12.5%
i 51105
11.0%
s 49400
10.6%
o 36105
 
7.7%
r 34466
 
7.4%
h 28251
 
6.1%
c 26991
 
5.8%
y 24428
 
5.2%
n 22372
 
4.8%
g 21456
 
4.6%
Other values (11) 113514
24.3%
Common
ValueCountFrequency (%)
. 39030
75.9%
9 5985
 
11.6%
4 4140
 
8.1%
6 2264
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 518069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 58562
 
11.3%
i 51105
 
9.9%
s 49400
 
9.5%
. 39030
 
7.5%
o 36105
 
7.0%
r 34466
 
6.7%
h 28251
 
5.5%
c 26991
 
5.2%
y 24428
 
4.7%
n 22372
 
4.3%
Other values (15) 147359
28.4%

default
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing391
Missing (%)0.9%
Memory size321.9 KiB
no
32274 
unknown
8520 
yes
 
3

Length

Max length7
Median length2
Mean length3.044268
Min length2

Characters and Unicode

Total characters124197
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowunknown
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 32274
78.4%
unknown 8520
 
20.7%
yes 3
 
< 0.1%
(Missing) 391
 
0.9%

Length

2025-02-15T00:15:42.721399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T00:15:42.853450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 32274
79.1%
unknown 8520
 
20.9%
yes 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 57834
46.6%
o 40794
32.8%
u 8520
 
6.9%
k 8520
 
6.9%
w 8520
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 124197
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 57834
46.6%
o 40794
32.8%
u 8520
 
6.9%
k 8520
 
6.9%
w 8520
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 124197
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 57834
46.6%
o 40794
32.8%
u 8520
 
6.9%
k 8520
 
6.9%
w 8520
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 57834
46.6%
o 40794
32.8%
u 8520
 
6.9%
k 8520
 
6.9%
w 8520
 
6.9%
y 3
 
< 0.1%
e 3
 
< 0.1%
s 3
 
< 0.1%

housing
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing379
Missing (%)0.9%
Memory size321.9 KiB
yes
21365 
no
18464 
unknown
 
980

Length

Max length7
Median length3
Mean length2.643608
Min length2

Characters and Unicode

Total characters107883
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
yes 21365
51.9%
no 18464
44.8%
unknown 980
 
2.4%
(Missing) 379
 
0.9%

Length

2025-02-15T00:15:43.005835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T00:15:43.136530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 21365
52.4%
no 18464
45.2%
unknown 980
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 21404
19.8%
y 21365
19.8%
e 21365
19.8%
s 21365
19.8%
o 19444
18.0%
u 980
 
0.9%
k 980
 
0.9%
w 980
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 107883
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 21404
19.8%
y 21365
19.8%
e 21365
19.8%
s 21365
19.8%
o 19444
18.0%
u 980
 
0.9%
k 980
 
0.9%
w 980
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 107883
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 21404
19.8%
y 21365
19.8%
e 21365
19.8%
s 21365
19.8%
o 19444
18.0%
u 980
 
0.9%
k 980
 
0.9%
w 980
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 21404
19.8%
y 21365
19.8%
e 21365
19.8%
s 21365
19.8%
o 19444
18.0%
u 980
 
0.9%
k 980
 
0.9%
w 980
 
0.9%

loan
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing455
Missing (%)1.1%
Memory size321.9 KiB
no
33575 
yes
6180 
unknown
 
978

Length

Max length7
Median length2
Mean length2.2717698
Min length2

Characters and Unicode

Total characters92536
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowyes
5th rowno

Common Values

ValueCountFrequency (%)
no 33575
81.5%
yes 6180
 
15.0%
unknown 978
 
2.4%
(Missing) 455
 
1.1%

Length

2025-02-15T00:15:43.281176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T00:15:43.406526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no 33575
82.4%
yes 6180
 
15.2%
unknown 978
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n 36509
39.5%
o 34553
37.3%
y 6180
 
6.7%
e 6180
 
6.7%
s 6180
 
6.7%
u 978
 
1.1%
k 978
 
1.1%
w 978
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92536
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 36509
39.5%
o 34553
37.3%
y 6180
 
6.7%
e 6180
 
6.7%
s 6180
 
6.7%
u 978
 
1.1%
k 978
 
1.1%
w 978
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 92536
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 36509
39.5%
o 34553
37.3%
y 6180
 
6.7%
e 6180
 
6.7%
s 6180
 
6.7%
u 978
 
1.1%
k 978
 
1.1%
w 978
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 36509
39.5%
o 34553
37.3%
y 6180
 
6.7%
e 6180
 
6.7%
s 6180
 
6.7%
u 978
 
1.1%
k 978
 
1.1%
w 978
 
1.1%

contact
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing440
Missing (%)1.1%
Memory size321.9 KiB
cellular
25847 
telephone
14901 

Length

Max length9
Median length8
Mean length8.3656867
Min length8

Characters and Unicode

Total characters340885
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular 25847
62.8%
telephone 14901
36.2%
(Missing) 440
 
1.1%

Length

2025-02-15T00:15:43.547067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T00:15:43.672165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 25847
63.4%
telephone 14901
36.6%

Most occurring characters

ValueCountFrequency (%)
l 92442
27.1%
e 70550
20.7%
c 25847
 
7.6%
u 25847
 
7.6%
a 25847
 
7.6%
r 25847
 
7.6%
t 14901
 
4.4%
p 14901
 
4.4%
h 14901
 
4.4%
o 14901
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 340885
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 92442
27.1%
e 70550
20.7%
c 25847
 
7.6%
u 25847
 
7.6%
a 25847
 
7.6%
r 25847
 
7.6%
t 14901
 
4.4%
p 14901
 
4.4%
h 14901
 
4.4%
o 14901
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 340885
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 92442
27.1%
e 70550
20.7%
c 25847
 
7.6%
u 25847
 
7.6%
a 25847
 
7.6%
r 25847
 
7.6%
t 14901
 
4.4%
p 14901
 
4.4%
h 14901
 
4.4%
o 14901
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 92442
27.1%
e 70550
20.7%
c 25847
 
7.6%
u 25847
 
7.6%
a 25847
 
7.6%
r 25847
 
7.6%
t 14901
 
4.4%
p 14901
 
4.4%
h 14901
 
4.4%
o 14901
 
4.4%

month
Categorical

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)< 0.1%
Missing421
Missing (%)1.0%
Memory size321.9 KiB
may
13625 
jul
7097 
aug
6120 
jun
5262 
nov
4056 
Other values (5)
4607 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters122301
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may 13625
33.1%
jul 7097
17.2%
aug 6120
14.9%
jun 5262
 
12.8%
nov 4056
 
9.8%
apr 2605
 
6.3%
oct 715
 
1.7%
sep 566
 
1.4%
mar 541
 
1.3%
dec 180
 
0.4%
(Missing) 421
 
1.0%

Length

2025-02-15T00:15:43.801441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T00:15:43.950194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
may 13625
33.4%
jul 7097
17.4%
aug 6120
15.0%
jun 5262
 
12.9%
nov 4056
 
9.9%
apr 2605
 
6.4%
oct 715
 
1.8%
sep 566
 
1.4%
mar 541
 
1.3%
dec 180
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 22891
18.7%
u 18479
15.1%
m 14166
11.6%
y 13625
11.1%
j 12359
10.1%
n 9318
7.6%
l 7097
 
5.8%
g 6120
 
5.0%
o 4771
 
3.9%
v 4056
 
3.3%
Other values (7) 9419
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 122301
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22891
18.7%
u 18479
15.1%
m 14166
11.6%
y 13625
11.1%
j 12359
10.1%
n 9318
7.6%
l 7097
 
5.8%
g 6120
 
5.0%
o 4771
 
3.9%
v 4056
 
3.3%
Other values (7) 9419
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 122301
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22891
18.7%
u 18479
15.1%
m 14166
11.6%
y 13625
11.1%
j 12359
10.1%
n 9318
7.6%
l 7097
 
5.8%
g 6120
 
5.0%
o 4771
 
3.9%
v 4056
 
3.3%
Other values (7) 9419
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22891
18.7%
u 18479
15.1%
m 14166
11.6%
y 13625
11.1%
j 12359
10.1%
n 9318
7.6%
l 7097
 
5.8%
g 6120
 
5.0%
o 4771
 
3.9%
v 4056
 
3.3%
Other values (7) 9419
7.7%

day_of_week
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing436
Missing (%)1.1%
Memory size321.9 KiB
thu
8526 
mon
8430 
wed
8049 
tue
7995 
fri
7752 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters122256
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu 8526
20.7%
mon 8430
20.5%
wed 8049
19.5%
tue 7995
19.4%
fri 7752
18.8%
(Missing) 436
 
1.1%

Length

2025-02-15T00:15:44.149074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T00:15:44.286237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
thu 8526
20.9%
mon 8430
20.7%
wed 8049
19.8%
tue 7995
19.6%
fri 7752
19.0%

Most occurring characters

ValueCountFrequency (%)
t 16521
13.5%
u 16521
13.5%
e 16044
13.1%
h 8526
7.0%
m 8430
6.9%
o 8430
6.9%
n 8430
6.9%
w 8049
6.6%
d 8049
6.6%
f 7752
6.3%
Other values (2) 15504
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 122256
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 16521
13.5%
u 16521
13.5%
e 16044
13.1%
h 8526
7.0%
m 8430
6.9%
o 8430
6.9%
n 8430
6.9%
w 8049
6.6%
d 8049
6.6%
f 7752
6.3%
Other values (2) 15504
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 122256
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 16521
13.5%
u 16521
13.5%
e 16044
13.1%
h 8526
7.0%
m 8430
6.9%
o 8430
6.9%
n 8430
6.9%
w 8049
6.6%
d 8049
6.6%
f 7752
6.3%
Other values (2) 15504
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 16521
13.5%
u 16521
13.5%
e 16044
13.1%
h 8526
7.0%
m 8430
6.9%
o 8430
6.9%
n 8430
6.9%
w 8049
6.6%
d 8049
6.6%
f 7752
6.3%
Other values (2) 15504
12.7%

campaign
Real number (ℝ)

MISSING 

Distinct42
Distinct (%)0.1%
Missing413
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean2.5669896
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2025-02-15T00:15:44.440156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7687599
Coefficient of variation (CV)1.0786019
Kurtosis37.08795
Mean2.5669896
Median Absolute Deviation (MAD)1
Skewness4.7665648
Sum104669
Variance7.6660315
MonotonicityNot monotonic
2025-02-15T00:15:44.803560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 17471
42.4%
2 10461
25.4%
3 5283
 
12.8%
4 2623
 
6.4%
5 1586
 
3.9%
6 973
 
2.4%
7 623
 
1.5%
8 392
 
1.0%
9 280
 
0.7%
10 225
 
0.5%
Other values (32) 858
 
2.1%
(Missing) 413
 
1.0%
ValueCountFrequency (%)
1 17471
42.4%
2 10461
25.4%
3 5283
 
12.8%
4 2623
 
6.4%
5 1586
 
3.9%
6 973
 
2.4%
7 623
 
1.5%
8 392
 
1.0%
9 280
 
0.7%
10 225
 
0.5%
ValueCountFrequency (%)
56 1
 
< 0.1%
43 2
 
< 0.1%
42 2
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
35 5
< 0.1%
34 3
< 0.1%
33 4
< 0.1%

pdays
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)0.1%
Missing449
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean962.34073
Minimum0
Maximum999
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2025-02-15T00:15:44.954486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation187.24291
Coefficient of variation (CV)0.19457029
Kurtosis22.129921
Mean962.34073
Median Absolute Deviation (MAD)0
Skewness-4.9120675
Sum39204799
Variance35059.908
MonotonicityNot monotonic
2025-02-15T00:15:45.103323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
999 39235
95.3%
3 436
 
1.1%
6 412
 
1.0%
4 117
 
0.3%
9 62
 
0.2%
2 60
 
0.1%
7 59
 
0.1%
12 57
 
0.1%
10 52
 
0.1%
5 46
 
0.1%
Other values (17) 203
 
0.5%
(Missing) 449
 
1.1%
ValueCountFrequency (%)
0 15
 
< 0.1%
1 26
 
0.1%
2 60
 
0.1%
3 436
1.1%
4 117
 
0.3%
5 46
 
0.1%
6 412
1.0%
7 59
 
0.1%
8 18
 
< 0.1%
9 62
 
0.2%
ValueCountFrequency (%)
999 39235
95.3%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
22 3
 
< 0.1%
21 2
 
< 0.1%
20 1
 
< 0.1%
19 3
 
< 0.1%
18 7
 
< 0.1%
17 8
 
< 0.1%

previous
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing418
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.17282315
Minimum0
Maximum7
Zeros35209
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2025-02-15T00:15:45.226861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49487273
Coefficient of variation (CV)2.8634631
Kurtosis20.133058
Mean0.17282315
Median Absolute Deviation (MAD)0
Skewness3.8344701
Sum7046
Variance0.24489902
MonotonicityNot monotonic
2025-02-15T00:15:45.357864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 35209
85.5%
1 4507
 
10.9%
2 746
 
1.8%
3 216
 
0.5%
4 68
 
0.2%
5 18
 
< 0.1%
6 5
 
< 0.1%
7 1
 
< 0.1%
(Missing) 418
 
1.0%
ValueCountFrequency (%)
0 35209
85.5%
1 4507
 
10.9%
2 746
 
1.8%
3 216
 
0.5%
4 68
 
0.2%
5 18
 
< 0.1%
6 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 5
 
< 0.1%
5 18
 
< 0.1%
4 68
 
0.2%
3 216
 
0.5%
2 746
 
1.8%
1 4507
 
10.9%
0 35209
85.5%

poutcome
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing431
Missing (%)1.0%
Memory size321.9 KiB
nonexistent
35188 
failure
4209 
success
 
1360

Length

Max length11
Median length11
Mean length10.453444
Min length7

Characters and Unicode

Total characters426051
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent 35188
85.4%
failure 4209
 
10.2%
success 1360
 
3.3%
(Missing) 431
 
1.0%

Length

2025-02-15T00:15:45.523600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-15T00:15:45.667280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent 35188
86.3%
failure 4209
 
10.3%
success 1360
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n 105564
24.8%
e 75945
17.8%
t 70376
16.5%
i 39397
 
9.2%
s 39268
 
9.2%
o 35188
 
8.3%
x 35188
 
8.3%
u 5569
 
1.3%
f 4209
 
1.0%
a 4209
 
1.0%
Other values (3) 11138
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 426051
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 105564
24.8%
e 75945
17.8%
t 70376
16.5%
i 39397
 
9.2%
s 39268
 
9.2%
o 35188
 
8.3%
x 35188
 
8.3%
u 5569
 
1.3%
f 4209
 
1.0%
a 4209
 
1.0%
Other values (3) 11138
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 426051
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 105564
24.8%
e 75945
17.8%
t 70376
16.5%
i 39397
 
9.2%
s 39268
 
9.2%
o 35188
 
8.3%
x 35188
 
8.3%
u 5569
 
1.3%
f 4209
 
1.0%
a 4209
 
1.0%
Other values (3) 11138
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 426051
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 105564
24.8%
e 75945
17.8%
t 70376
16.5%
i 39397
 
9.2%
s 39268
 
9.2%
o 35188
 
8.3%
x 35188
 
8.3%
u 5569
 
1.3%
f 4209
 
1.0%
a 4209
 
1.0%
Other values (3) 11138
 
2.6%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)< 0.1%
Missing418
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.082460142
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative17010
Negative (%)41.3%
Memory size321.9 KiB
2025-02-15T00:15:45.785861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5707488
Coefficient of variation (CV)19.048582
Kurtosis-1.0613797
Mean0.082460142
Median Absolute Deviation (MAD)0.3
Skewness-0.72485359
Sum3361.9
Variance2.4672518
MonotonicityNot monotonic
2025-02-15T00:15:45.906862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 16073
39.0%
-1.8 9081
22.0%
1.1 7687
18.7%
-0.1 3646
 
8.9%
-2.9 1646
 
4.0%
-3.4 1060
 
2.6%
-1.7 769
 
1.9%
-1.1 629
 
1.5%
-3 169
 
0.4%
-0.2 10
 
< 0.1%
(Missing) 418
 
1.0%
ValueCountFrequency (%)
-3.4 1060
 
2.6%
-3 169
 
0.4%
-2.9 1646
 
4.0%
-1.8 9081
22.0%
-1.7 769
 
1.9%
-1.1 629
 
1.5%
-0.2 10
 
< 0.1%
-0.1 3646
 
8.9%
1.1 7687
18.7%
1.4 16073
39.0%
ValueCountFrequency (%)
1.4 16073
39.0%
1.1 7687
18.7%
-0.1 3646
 
8.9%
-0.2 10
 
< 0.1%
-1.1 629
 
1.5%
-1.7 769
 
1.9%
-1.8 9081
22.0%
-2.9 1646
 
4.0%
-3 169
 
0.4%
-3.4 1060
 
2.6%

cons.price.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing369
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean93.575781
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2025-02-15T00:15:46.037916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.57895831
Coefficient of variation (CV)0.006187053
Kurtosis-0.83081654
Mean93.575781
Median Absolute Deviation (MAD)0.38
Skewness-0.23063369
Sum3819669.8
Variance0.33519273
MonotonicityNot monotonic
2025-02-15T00:15:46.180774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 7697
18.7%
93.918 6621
16.1%
92.893 5751
14.0%
93.444 5130
12.5%
94.465 4341
10.5%
93.2 3574
8.7%
93.075 2435
 
5.9%
92.201 762
 
1.9%
92.963 707
 
1.7%
92.431 441
 
1.1%
Other values (16) 3360
8.2%
(Missing) 369
 
0.9%
ValueCountFrequency (%)
92.201 762
 
1.9%
92.379 266
 
0.6%
92.431 441
 
1.1%
92.469 177
 
0.4%
92.649 352
 
0.9%
92.713 172
 
0.4%
92.756 10
 
< 0.1%
92.843 281
 
0.7%
92.893 5751
14.0%
92.963 707
 
1.7%
ValueCountFrequency (%)
94.767 127
 
0.3%
94.601 203
 
0.5%
94.465 4341
10.5%
94.215 308
 
0.7%
94.199 299
 
0.7%
94.055 224
 
0.5%
94.027 232
 
0.6%
93.994 7697
18.7%
93.918 6621
16.1%
93.876 212
 
0.5%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.1%
Missing404
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-40.504127
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative40784
Negative (%)99.0%
Memory size321.9 KiB
2025-02-15T00:15:46.318024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.6248248
Coefficient of variation (CV)-0.11418157
Kurtosis-0.36164489
Mean-40.504127
Median Absolute Deviation (MAD)4.4
Skewness0.30090646
Sum-1651920.3
Variance21.389004
MonotonicityNot monotonic
2025-02-15T00:15:46.462694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 7698
18.7%
-42.7 6621
16.1%
-46.2 5734
13.9%
-36.1 5124
12.4%
-41.8 4336
10.5%
-42 3585
8.7%
-47.1 2431
 
5.9%
-31.4 761
 
1.8%
-40.8 704
 
1.7%
-26.9 438
 
1.1%
Other values (16) 3352
8.1%
(Missing) 404
 
1.0%
ValueCountFrequency (%)
-50.8 127
 
0.3%
-50 281
 
0.7%
-49.5 201
 
0.5%
-47.1 2431
 
5.9%
-46.2 5734
13.9%
-45.9 10
 
< 0.1%
-42.7 6621
16.1%
-42 3585
8.7%
-41.8 4336
10.5%
-40.8 704
 
1.7%
ValueCountFrequency (%)
-26.9 438
 
1.1%
-29.8 261
 
0.6%
-30.1 353
 
0.9%
-31.4 761
 
1.8%
-33 171
 
0.4%
-33.6 177
 
0.4%
-34.6 173
 
0.4%
-34.8 261
 
0.6%
-36.1 5124
12.4%
-36.4 7698
18.7%

euribor3m
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct316
Distinct (%)0.8%
Missing429
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean3.6206528
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2025-02-15T00:15:46.624977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.7346196
Coefficient of variation (CV)0.4790903
Kurtosis-1.4077061
Mean3.6206528
Median Absolute Deviation (MAD)0.108
Skewness-0.70850689
Sum147574.19
Variance3.0089053
MonotonicityNot monotonic
2025-02-15T00:15:46.794877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.857 2837
 
6.9%
4.962 2587
 
6.3%
4.963 2465
 
6.0%
4.961 1881
 
4.6%
4.856 1193
 
2.9%
4.964 1164
 
2.8%
1.405 1155
 
2.8%
4.965 1060
 
2.6%
4.864 1036
 
2.5%
4.96 1009
 
2.4%
Other values (306) 24372
59.2%
ValueCountFrequency (%)
0.634 8
 
< 0.1%
0.635 43
0.1%
0.636 14
 
< 0.1%
0.637 6
 
< 0.1%
0.638 7
 
< 0.1%
0.639 16
 
< 0.1%
0.64 9
 
< 0.1%
0.642 34
0.1%
0.643 23
0.1%
0.644 38
0.1%
ValueCountFrequency (%)
5.045 9
 
< 0.1%
5 7
 
< 0.1%
4.97 170
 
0.4%
4.968 976
 
2.4%
4.967 630
 
1.5%
4.966 610
 
1.5%
4.965 1060
2.6%
4.964 1164
2.8%
4.963 2465
6.0%
4.962 2587
6.3%

nr.employed
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)< 0.1%
Missing437
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean5167.0627
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2025-02-15T00:15:46.931499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.224169
Coefficient of variation (CV)0.0139778
Kurtosis-0.00028603709
Mean5167.0627
Median Absolute Deviation (MAD)37.1
Skewness-1.0450743
Sum2.1056297 × 108
Variance5216.3306
MonotonicityNot monotonic
2025-02-15T00:15:47.060326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 16062
39.0%
5099.1 8451
20.5%
5191 7696
18.7%
5195.8 3633
 
8.8%
5076.2 1640
 
4.0%
5017.5 1058
 
2.6%
4991.6 760
 
1.8%
5008.7 640
 
1.6%
4963.6 631
 
1.5%
5023.5 170
 
0.4%
(Missing) 437
 
1.1%
ValueCountFrequency (%)
4963.6 631
 
1.5%
4991.6 760
 
1.8%
5008.7 640
 
1.6%
5017.5 1058
 
2.6%
5023.5 170
 
0.4%
5076.2 1640
 
4.0%
5099.1 8451
20.5%
5176.3 10
 
< 0.1%
5191 7696
18.7%
5195.8 3633
8.8%
ValueCountFrequency (%)
5228.1 16062
39.0%
5195.8 3633
 
8.8%
5191 7696
18.7%
5176.3 10
 
< 0.1%
5099.1 8451
20.5%
5076.2 1640
 
4.0%
5023.5 170
 
0.4%
5017.5 1058
 
2.6%
5008.7 640
 
1.6%
4991.6 760
 
1.8%

y
Boolean

Distinct2
Distinct (%)< 0.1%
Missing398
Missing (%)1.0%
Memory size80.6 KiB
False
36199 
True
4591 
(Missing)
 
398
ValueCountFrequency (%)
False 36199
87.9%
True 4591
 
11.1%
(Missing) 398
 
1.0%
2025-02-15T00:15:47.186826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2025-02-15T00:15:39.055826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:29.878280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:31.061301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:32.132033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:33.379750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:34.492779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:35.556178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:36.620466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:37.740624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:39.184403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:30.058145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:31.188879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:32.266309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:33.515145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:34.622382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:35.685869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:36.750781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:37.892842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:39.303780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:30.185128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:31.307876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:32.392642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:33.641289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:34.739582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:35.805685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:36.886066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:38.040239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:39.420399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:30.306689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:31.425823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:32.515249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:33.762323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:34.857109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:35.922208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:37.018813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:38.361153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:39.565706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:30.437898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:31.550346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:32.649249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:33.889340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:34.988292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:36.048306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:37.146608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:38.483815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:39.687688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:30.558573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:31.666421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:32.767794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:34.010821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:35.101823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:36.161814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:37.261660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:38.603377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:39.802289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:30.680467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:31.781421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:32.888730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:34.131842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:35.216875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:36.273865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:37.375616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:38.715538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:39.918135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:30.813461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:31.896417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:33.146766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:34.252474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:35.332875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:36.388161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:37.489659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:38.830248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:40.031677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:30.935169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:32.013050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:33.263788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:34.372444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:35.442606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:36.503565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:37.612250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-02-15T00:15:38.941081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-02-15T00:15:47.299560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
agecampaigncons.conf.idxcons.price.idxcontactday_of_weekdefaulteducationemp.var.rateeuribor3mhousingjobloanmaritalmonthnr.employedpdayspoutcomepreviousy
age1.0000.0060.1140.0450.0990.0240.1460.1170.0460.0560.0000.2490.0100.2630.0930.047-0.0010.107-0.0130.172
campaign0.0061.000-0.0020.0950.0650.0180.0160.0040.1560.1400.0230.0000.0210.0000.0470.1420.0550.047-0.0870.051
cons.conf.idx0.114-0.0021.0000.2470.4180.0450.1380.0640.2250.2370.0420.1090.0120.0720.6000.134-0.0780.369-0.1150.386
cons.price.idx0.0450.0950.2471.0000.6760.0500.1540.0980.6640.4920.0690.1310.0170.0690.6760.4650.0580.387-0.2820.337
contact0.0990.0650.4180.6761.0000.0550.1370.1240.2310.1410.0840.1270.0230.0730.6090.1090.1180.243-0.2420.145
day_of_week0.0240.0180.0450.0500.0551.0000.0110.0190.0300.0290.0160.0170.0070.0110.0670.026-0.0110.016-0.0080.024
default0.1460.0160.1380.1540.1370.0111.0000.1700.1780.1690.0110.1510.0040.0950.1120.1570.0800.076-0.1050.098
education0.1170.0040.0640.0980.1240.0190.1701.000-0.018-0.0020.0140.3600.0000.1170.095-0.010-0.0500.0430.0330.068
emp.var.rate0.0460.1560.2250.6640.2310.0300.178-0.0181.0000.9400.0520.1350.0120.0670.6590.9450.2280.380-0.4350.342
euribor3m0.0560.1400.2370.4920.1410.0290.169-0.0020.9401.0000.0530.1280.0130.0680.5520.9290.2790.418-0.4550.400
housing0.0000.0230.0420.0690.0840.0160.0110.0140.0520.0531.0000.0090.7080.0090.054-0.035-0.0100.0160.0250.010
job0.2490.0000.1090.1310.1270.0170.1510.3600.1350.1280.0091.0000.0110.1830.109-0.000-0.0170.0990.0080.152
loan0.0100.0210.0120.0170.0230.0070.0040.0000.0120.0130.7080.0111.0000.0000.0190.0050.0020.0000.0010.000
marital0.2630.0000.0720.0690.0730.0110.0950.1170.0670.0680.0090.1830.0001.0000.050-0.071-0.0380.0370.0380.055
month0.0930.0470.6000.6760.6090.0670.1120.0950.6590.5520.0540.1090.0190.0501.000-0.364-0.0490.2430.1290.274
nr.employed0.0470.1420.1340.4650.1090.0260.157-0.0100.9450.929-0.035-0.0000.005-0.071-0.3641.0000.2910.413-0.4390.409
pdays-0.0010.055-0.0780.0580.118-0.0110.080-0.0500.2280.279-0.010-0.0170.002-0.038-0.0490.2911.0000.681-0.5110.329
poutcome0.1070.0470.3690.3870.2430.0160.0760.0430.3800.4180.0160.0990.0000.0370.2430.4130.6811.000-0.4950.320
previous-0.013-0.087-0.115-0.282-0.242-0.008-0.1050.033-0.435-0.4550.0250.0080.0010.0380.129-0.439-0.511-0.4951.0000.236
y0.1720.0510.3860.3370.1450.0240.0980.0680.3420.4000.0100.1520.0000.0550.2740.4090.3290.3200.2361.000

Missing values

2025-02-15T00:15:40.223221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-15T00:15:40.595760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-15T00:15:41.074495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
056.0housemaidmarriedbasic.4ynononotelephonemaymon1.0999.00.0nonexistent1.193.994-36.44.8575191.0no
157.0servicesmarriedhigh.schoolunknownnoNaNtelephonemaymon1.0999.00.0nonexistent1.193.994-36.44.8575191.0no
237.0servicesmarriedhigh.schoolnoyesnotelephonemaymon1.0999.00.0nonexistent1.193.994-36.44.8575191.0no
340.0admin.marriedbasic.6ynononotelephonemaymon1.0999.00.0nonexistent1.193.994-36.44.8575191.0no
456.0servicesmarriedhigh.schoolnonoyesNaNmaymon1.0999.00.0nonexistent1.193.994-36.44.8575191.0no
545.0servicesmarriedbasic.9yunknownnonotelephonemaymon1.0999.00.0nonexistent1.193.994-36.44.8575191.0no
659.0admin.marriedprofessional.coursenononotelephonemaymon1.0999.00.0nonexistent1.193.994-36.44.8575191.0no
741.0blue-collarmarriedunknownunknownnonotelephonemaymon1.0999.00.0nonexistentNaN93.994-36.44.8575191.0no
824.0techniciansingleprofessional.coursenoyesnotelephonemaymon1.0999.00.0nonexistent1.193.994-36.44.8575191.0no
925.0servicessinglehigh.schoolnoNaNnotelephonemaymon1.0999.00.0nonexistent1.193.994-36.44.8575191.0no
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
4117862.0retiredmarrieduniversity.degreenononocellularnovthu2.06.03.0success-1.194.767-50.81.0314963.6yes
4117964.0retireddivorcedprofessional.coursenoyesnocellularnovfri3.0999.00.0nonexistent-1.194.767-50.81.0284963.6no
4118036.0admin.marrieduniversity.degreenononocellularnovfri2.0999.00.0nonexistent-1.194.767-50.8NaN4963.6no
4118137.0admin.marrieduniversity.degreenoyesnocellularnovfri1.0999.00.0nonexistent-1.194.767-50.81.0284963.6yes
4118229.0unemployedsinglebasic.4ynoyesnoNaNnovfri1.09.01.0success-1.194.767-50.81.0284963.6no
4118373.0retiredmarriedprofessional.coursenoyesnocellularnovfri1.0999.00.0nonexistent-1.194.767-50.81.0284963.6yes
4118446.0blue-collarmarriedprofessional.coursenononocellularnovfri1.0999.0NaNnonexistent-1.194.767-50.81.0284963.6no
4118556.0retiredmarrieduniversity.degreenoyesnocellularnovfri2.0999.00.0nonexistent-1.194.767-50.81.0284963.6no
4118644.0technicianmarriedprofessional.coursenononocellularnovfri1.0999.00.0nonexistent-1.194.767-50.81.0284963.6yes
4118774.0retiredmarriedprofessional.coursenoyesnocellularnovfri3.0999.01.0failure-1.194.767-50.81.0284963.6no

Duplicate rows

Most frequently occurring

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy# duplicates
4827.0admin.singlehigh.schoolnononocellularjultue1.0999.00.0nonexistent1.493.918-42.74.9625228.1no7
77243.0housemaidmarriedbasic.4ynoyesnocellularjulwed1.0999.00.0nonexistent1.493.918-42.74.9625228.1no6
8528.0servicesmarriedhigh.schoolnononocellularjultue1.0999.00.0nonexistent1.493.918-42.74.9625228.1no5
16230.0admin.singlehigh.schoolnononocellularaugfri1.0999.00.0nonexistent1.493.444-36.14.9645228.1no5
21731.0admin.marrieduniversity.degreenononocellularnovthu1.0999.00.0nonexistent-0.193.200-42.04.0765195.8no5
52936.0blue-collarmarriedbasic.9yunknownyesnocellularjulthu1.0999.00.0nonexistent1.493.918-42.74.9635228.1no5
1524.0admin.singlehigh.schoolunknownyesnocellularjulthu1.0999.00.0nonexistent1.493.918-42.74.9585228.1no4
1924.0servicessingleprofessional.coursenoyesnocellularjulwed1.0999.00.0nonexistent1.493.918-42.74.9625228.1no4
2024.0studentsinglebasic.4ynononocellularjulfri1.0999.00.0nonexistent1.493.918-42.74.9635228.1no4
2124.0studentsinglehigh.schoolunknownnonocellularjultue1.0999.00.0nonexistent1.493.918-42.74.9625228.1no4